Machine Learning models which will be able to predict material properties from experimental / simulation data.
Development of data-driven and expert knowledge-driven machine learning models and to predict material properties and optimize materials properties and processes.

The procedures and methodologies used in our machine learning-driven material property prediction services adhere to industry-recognized standards and best practices. While AI-driven material predictions are an evolving field, we ensure compliance with relevant international and European standards where applicable. Additionally, our models and methodologies align with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, ensuring transparency and reproducibility in data-driven material property predictions.

Our service leverages cutting-edge machine learning algorithms to predict material properties with high accuracy, reducing the need for extensive physical testing and accelerating material innovation. By integrating data-driven and expert knowledge-driven AI models, we enable precise optimization of material compositions and processes, enhancing efficiency and sustainability. The use of digital twin technology allows for real-time simulation and iterative improvements, significantly reducing development time and costs. Additionally, our approach aligns with FAIR data principles, ensuring transparency, reproducibility, and interoperability in material research. This innovative methodology not only streamlines R&D in materials science but also fosters the development of eco-friendly, high-performance materials, supporting the transition towards a more sustainable and resource-efficient industry.

Pricing

Case by Case

Our company does not apply cost-plus pricing but value based pricing. Therefore the expected cost of delivery will both depend on the scope of the requested service as well as the value the project will bring to the customer.

Service delivery timeframe

30-60 Woeking Days